• Drive architectural direction across the product line, design and implement highly scalable microservices, well-crafted RESTful APIs, and event-driven systems spanning on-premises .NET desktop applications and Kubernetes hosted cloud services. • Improve performance, reliability, and observability across product offerings, and proactively identify architectural risks before they become incidents. • Establish system design patterns, service standards, and architectural guidelines that teams can adopt consistently. • Inspire and lead teams through change by clearly communicating technical vision, trade-offs, and direction in design reviews, architecture discussions, and technical presentations. • Work across multiple engineering teams to shape technical direction through collaboration and earned trust, leading cross-functional initiatives that reduce technical debt and improve long-term maintainability. • Partner with product and engineering leadership to align technical architecture with product and feature roadmaps. • Guide features end-to-end from concept through requirements, development, security review, compliance, support readiness, and release, working with engineers to refine and deliver them. • Jump in alongside engineers on complex problems, code reviews, and design discussions — take ownership in their success and growth as if it were your own. • Help teams build confidence in iterative delivery, straightforward patterns get us most of the way there, and software that is simple and well-understood is software we can change without fear. • Help shape how our teams adopt AI coding assistants and emerging technologies by designing the workflows, standards, and feedback loops that turn these tools into genuine productivity multipliers rather than sources of technical debt. • Collaborate with senior leaders and teams to evolve engineering processes, tooling, and standards in ways that fit our culture and growth stage. • Own security posture across the platform: drive secure design practices, threat modeling, and defense-in-depth strategies, and contribute to compliance initiatives and audit readiness as we serve customers across regulated industries and government.

DeepInfra is looking for strong Software Engineers to join our team. You’ll work on designing, building, and scaling infrastructure for serving top open-source AI models in production. This role is ideal for engineers who are already comfortable owning problems end-to-end and want to deepen their experience working on high-impact AI systems. If you’re excited about AI/ML and are looking to work on real systems at scale — we’d love to meet you. What You’ll Do - Design, develop, and test inference solutions for state-of-the-art AI models - Implement, optimize, and evaluate AI models using Python, C++, CUDA, and NCCL - Own and operate production model-serving systems, including monitoring and debugging - Build new features, improve system performance, and contribute to overall system design - Participate in code reviews and technical discussions to maintain high engineering standards - Explore and apply new AI/ML techniques to improve model performance and efficiency - Take ideas from concept to production What You Bring - Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field - 3+ years of relevant experience - Strong fundamentals in data structures, algorithms, and software design - Proficiency in Python and experience working with AI/ML frameworks (e.g., PyTorch, TensorFlow) - Hands-on experience building, shipping, and maintaining software systems - Familiarity with AI models, Transformers, and Diffusers - Experience working with version control (Git) and collaborative development workflows - Ability to debug, optimize, and improve existing systems - Strong communication skills and ability to work independently in a fast-paced environment Bonus - Experience with C++, CUDA, or AI inference - Contributions to open-source ML projects Why DeepInfra - Work on cutting-edge AI model serving - the systems that power the next generation of LLMs and multimodal models. - Small team, huge impact: your work ships directly to customers. - Opportunity to learn from engineers building high-performance inference at scale. - Fast-paced environment with ownership, autonomy, and end-to-end responsibility.

• Develop, deploy and monitor feature computation and analytics services that transform raw biosignal data, machine learning outputs, and clinical metadata to generate meaningful scientific results • Build high-quality composable tooling that enables data scientists to deliver scientific results at scale • Transform custom or semi-standardized data workflows into reusable tools and automated products that power clinical diagnostics and trials • Design, implement, and maintain versioned data models and transformations that enrich biosignal metrics with clinical context • Contribute to reusable, scalable customer-facing analytics products, such as automated scientific reports, dashboards and purpose-specific dataset views • Collaborate closely with teams of engineers, data scientists, and neuroscientists to understand workflow pain-points and inefficiencies that can be resolved with tooling and processes improvements • Partner with scientists, product managers, and stakeholders to identify high-impact analytics improvements that accelerate development of novel therapies and diagnostic tools

• Write performant, maintainable code that is easy to read and well-documented. • Write automated tests to thoroughly validate the correctness of your code. • Deploy large-scale web applications. • Have a focus on quality, and operate iteratively to get from good to better. • Be a part of your team’s on-call rotation; we all own our code end-to-end from development to deployment to operations. • Come ready to learn from some of the best and brightest in the industry. • Work independently and collaboratively in a dynamic and fast-moving team. • Be effective in a team environment working with fellow engineers, product management, and design.